1. Field of the Invention
The present invention relates to a signal processor having an improved signal to noise ratio. More specifically, the present invention relates to a signal processor which uses amplitude temporal fluctuations of a received signal to apply a power-law operation, whereby the power-law operation changes based upon whether the amplitude temporal fluctuations indicate that sampled data of the received signal relate to noise or clutter, or whether the sampled data relates to a signal representative of an object.
2. Description of the Related Art
For signals traveling through a propagation pathway, inherently, the surrounding environment causes amplitude temporal fluctuations to be generated in the signals. There is no way to avoid such amplitude temporal fluctuations. In many cases, these fluctuations may cause degraded performance in signal processors attempting to receive or interpret such signals.
To minimize such fluctuations, several methods have been applied, such as power averaging of the received signal for a time duration much longer than the fluctuation periods and carefully designing signal measurements to reduce the effects of the mechanisms that generated the fluctuations. For example, in an underwater acoustic wave environment, a particular acoustic propagation condition (e.g., propagation path, frequency, source and receiver depths, and acoustic environment) and fluctuation generation mechanisms encountered during propagation (e.g., internal waves and reflection from the moving sea surface) effectively xe2x80x9cencodexe2x80x9d both the acoustic signal and noise with a unique fluctuation character. The net effect of this fluctuation character, for underwater acoustic signal processors (e.g., sonar), is to induce temporal variations, or fluctuations, in the phases and amplitudes of the acoustic signals and noise. The induced fluctuations individualize the various acoustic signals and noise. For example, acoustic signals from a submerged acoustic source in the ocean will have smaller fluctuations than ambient noise and signals from acoustic sources near the sea surface. This individuality can be identified by a fluctuation sensitive acoustic signal processor and used to enhance aspects of its performance, such as improving the detectability of the signal by reducing the noise and clutter, thereby increasing the signal-to-noise ratio (SNR).
It has long been recognized that fluctuations in the amplitudes of received signals and noise can have an important influence on the performance of an acoustic signal receiver. Research has shown that the presence of fluctuations actually can extend the acoustic detection range of an underwater acoustic signal receiver, e.g., sonar receiver on a submarine. Previously, it has been suggested that the amplitude distribution of the acoustic signal from a submerged source can provide a clue to determining the propagation paths between the submerged source and acoustic signal receiver and hence, the depth of the submerged source. For example, in a submarine environment it may be beneficial to actually utilize the fluctuations to increase the range for detecting objects, both submerged and on the water surface. However, outside of these two noteworthy exceptions, the general rule has been that fluctuations are considered to be a nuisance that degrade an acoustic signal processor""s performance, as well as signal processors designed for alternative signal types, and should be ignored, reduced, or avoided whenever possible.
Extensive theoretical and empirical studies have been conducted into the mechanisms that cause or generate amplitude fluctuations in the underwater acoustic environment. Some of the causes that are considered to be the most important, for periods of a few minutes or less, include the following: thermal and salinity finestructure; internal waves; turbulent particle velocities; ray-path or wave-front reflection from the moving irregular sea surface; source-receiver range separation changing; source and/or receiver vertical motion causing temporal changes in modes or ray-paths; source radiation amplitude instability; and influence of multi-path arrivals.
The causes of amplitude fluctuations mentioned above are not comprehensive of all causes that are known, nor do all apply to every type of propagation media, such as for optical signal propagation, electrical signal propagation, magnetic signal propagation, or even for electromagnetic signal propagation. The present invention is broadly applicable to all of the aforementioned environments, but herein we will only discuss it""s applicability to underwater acoustic propagation. The above fluctuations generating mechanisms listed above are simply those that are believed to be the most important in underwater acoustic propagation, for the time scales of a few minutes or less for the fluctuation-based acoustic signal processors considered herein. Nevertheless, the present invention applies to those other propagation environments with their corresponding fluctuation generation mechanisms and fluctuation time scales.
The present invention, including a corresponding acoustic signal processor, is set forth herein using underwater acoustic data. And as noted above, similar principles, applications, and results would be expected for its use in other propagation media.
Typically, a source may emit a known acoustic signal into an underwater environment, and thereafter receive the reflection of that acoustic signal as the acoustic signal returns to the source. The returning signal will typically indicate the presence of an object based upon the amount of time the acoustic signal was in transit and the direction from which the acoustic signal returned to the source. Usually, a submerged source, as well as surface ships, utilize a group of hydrophones that can be dragged behind the submerged source. With the distance between hydrophones being known and the signal strengths of each returning acoustic signal detected by the individual hydrophones being known, a 3-dimensional direction of the return path of the returning acoustic signal can be calculated. Based on the calculated return path and known time lapse, an actual positioning of an object can be determined. If the actual position of the object is known, then a comparison between the detected position and the known position can show these aforementioned temporal fluctuations, where the fluctuation mechanisms xe2x80x9cencodexe2x80x9d the returning acoustic signal with a fluctuation character. Here, where the source knows what acoustic signal was sent out, e.g. a ramp, knows what acoustic signal to expect in return, i.e., the ramp, and knows the positioning of the detected object, then these fluctuations can be detected. Within the detected fluctuations there will be low fluctuation amplitude (LOFA) signals and high fluctuation amplitude (HIFA) clutter signals, where the HIFA signals are usually of most concern, as their high amplitude overpowers the acoustic signal. Typically, though the source may be passively xe2x80x9clisteningxe2x80x9d and would not know the character, e.g., the ramp, of the acoustic signal that it is receiving nor the positioning of objects in the underwater environment. With this dilemma in mind, the present invention overcomes these difficulties by using an acoustic signal processor to detect and utilize the differences between these LOFA and HIFA signals for increased signal to noise performance.
In order to design an acoustic signal processor that will detect LOFA signals against a competing background of ambient noise and HIFA clutter signals from surface objects, such as ships, some feature, characteristic, or property of the acoustic signal must be identified, which is different for the LOFA signals than it is for the noise and other HIFA signals that are not of interest. An example of a characteristic that has been focused on by a very common signal processor AVGPR (average power or power-law processor) is the acoustic signal amplitude at a given frequency. As noted above, it is a practice in determining the characteristic of an acoustic propagation path to project a high amplitude acoustic signal at a known frequency or frequency range, such as the ramp, and then to identify the returning acoustic signal in the acoustic signal processor""s output by its large output signal to noise ratio. Unfortunately, LOFA signals at unknown frequencies can not be distinguished from HIFA signals by amplitude, as high or low amplitudes for LOFA and HIFA signals are not unique to one or the other.
The present invention solves these aforementioned problems by distinguishing LOFA signals from HIFA signals and noise, based on amplitude fluctuations in the received signal to provide a preferential gain for the LOFA signals.
An object of the present invention is to distinguish between LOFA signals and HIFA signals and noise using an acoustic signal processor, with the acoustic signal processor being sensitive to amplitude fluctuations.
A further object of the present invention is to use the squares of successive differences in temporary successive log amplitudes to accomplish a preferential signal to noise ratio increase.
Another object of the present invention is to implement a signal processing method, including receiving a plurality of data samples, and filtering a first group of the plurality of received data samples for a first signal by applying a power-law arithmetic operation to the first group of data samples, such that the power-law arithmetic operation is adaptively changed based on an order dependent determination of whether the first group of the data samples is representative of noise or clutter.
Another object of the present invention is to implement an apparatus for filtering noise from a received signal to improve the signal to noise ratio of the received signal, including a processor programmed to filter data samples of the received signal by applying a power-law arithmetic operation to the data samples, such that the power-law arithmetic operation is adaptively changed based on an order dependent determination of whether the data samples are representative of noise or clutter.
A further object of the present invention is to implement an acoustic measuring method, including sampling acoustic data, spectrum analyzing the sampled acoustic data, storing the analyzed acoustic data in corresponding frequency bins, and filtering acoustic data stored in a first frequency bin by applying a power-law arithmetic operation to the first frequency bin acoustic data, such that the power-law arithmetic operation is adaptively changed based on an order dependent determination of whether the first frequency bin acoustic data is representative of noise or clutter.